Gabrielle Alcaire

° Maintained >95% SLA compliance, managing incident response and status communications for critical systems. ° Analyzed BigQuery/GCP metrics and logs to monitor health and detect early degradation before SLA impact. ° Escalated bugs to L3 Engineering with reproducible cases, shortening the diagnosis-to-fix cycle. ° Built AI tools to streamline documentation interpretation, boosting team response speed. ° Delivered trilingual support (PT/EN/ES) globally and mentored L1 analysts through shadowing.

° Resolved 50-150 cases/month (80-90% CSAT), fixing API integration and configuration errors. ° Used SQL, logs, and console inspection to isolate root causes and reduce L3 escalations. ° Executed SQL queries and backups to validate cross-environment data consistency. ° Performed QA on system updates prior to production to ensure release stability. ° Led API training workshops, improving team resolution speed for integration tickets. ° Documented internal procedures to standardize support delivery.

° Managed a portfolio of 20 client accounts, overseeing contract renewals, plan negotiations, and platform adoption. ° Negotiated plan upgrades (upsell) that contributed to portfolio revenue growth. ° Represented Voice of the Customer (VoC) in internal meetings, translating client feedback into product and process improvement recommendations. ° Delivered platform demos and training sessions for external clients and internal teams. ° Maintained CRM data integrity for the full account portfolio.

° Managed support tickets via Jira, maintaining SLA compliance across L1/L2 incident triage and escalation. ° Performed data validation and cleansing for high-volume client data loads into the Nimbi platform. ° Created and maintained user manuals and Knowledge Base documentation, improving support consistency. ° Configured and maintained Production and QA environments to support client implementation and testing. ° Led virtual training sessions on platform processes for internal and external users.

Specialization focused on the full data lifecycle, covering relational and non-relational database architectures, data lake and data warehouse design, distributed processing frameworks (Spark, Kafka, Hadoop), and cloud-based deployment of data pipelines and ML models across AWS, GCP, and Azure.